Automation That Handles the Messy Parts

n8n & Make.com workflows with AI doing the classification, extraction, and routing your team does manually today.

Sound Familiar?

This Is Probably You

Your team is talented. But they're spending hours on work that software should handle. These are solvable problems.

Someone is the unofficial 'Zapier person' everyone depends on

Automations break weekly and nobody knows why

Data moves between systems via copy-paste or CSV

CRM, helpdesk, and spreadsheets never agree on the numbers

Know AI could help but unsure where to wire it in

Results

Outcomes From Real Engagements

4 hrs/day

Saved automating invoice processing with n8n + GPT-4

90%

Support tickets classified correctly on first pass with Make.com + AI

Zero

Manual CSV exports after connecting 4 systems with n8n sync

“Our service desk AI handles 40% of L1 tickets automatically now. The agents focus on real problems.”

Daniel Okonkwo

VP Customer Success, TechNova Solutions

What We Do

Audit. Build. Stabilize.

Three engagement types that match where you are today.

1-2 weeks

Audit

Map your workflows, find the bottlenecks, and deliver a prioritized opportunity report.

Current workflow documentation

Bottleneck and failure point analysis

Tool and integration inventory

Prioritized automation roadmap

4-8 weeks

Build

Design and deploy n8n or Make.com workflows with AI touchpoints where they matter.

Workflow architecture and design

n8n / Make.com implementation

AI node integration (classification, extraction)

Testing and iteration with your team

2-4 weeks

Stabilize

Harden existing automations. Add error handling, monitoring, and documentation.

Error handling and retry logic

Monitoring and alerting setup

Documentation and runbooks

Team training and handoff

Use Cases

Common Automations by Function

Real examples of what we build across departments.

Lead Scoring & Routing

Auto-score inbound leads and route to the right rep based on fit signals

Proposal Generation

Pull CRM data into templated proposals with AI-written summaries

Follow-up Triggers

Automated sequences triggered by deal stage changes or inactivity

Pipeline Reporting

Daily Slack digest with pipeline changes, risk flags, and next actions

AI Integration

Where AI Fits In

Most automation is rules-based — and it should be. Simple logic is reliable, fast, and cheap. AI gets added only where it provides genuine value: handling unstructured data, making judgment calls, and understanding intent.

Everywhere else, simple logic wins. We don't add AI for the sake of AI. We add it where it removes manual work that rules can't handle.

AI-Powered Nodes

Classify incoming requests by intent

Extract fields from unstructured documents

Route items based on content analysis

Summarize threads and conversations

Generate draft responses from context

The Process

How an Engagement Works

A focused, iterative process that keeps your team in the loop.

Week 1

Map

Document workflows, systems, and pain points

Week 1-2

Design

Architect automation with AI touchpoints, define metrics

Week 2-6

Deploy

Build, test, iterate with your team in the loop

Week 6-8

Harden

Error handling, monitoring, documentation, knowledge transfer

Is This You?

Good Fit vs. Not a Fit

Great fit if…

Teams drowning in manual, repetitive work

Operations running on spreadsheets and email

5-50 SaaS tools that don't talk to each other

Probably not a fit if…

Need a single Zapier zap set up

Want a custom platform built from scratch

Looking for tool recommendations without implementation

Automation FAQ

Frequently Asked Questions

Stop Copy-Pasting. Start Automating.

Start with an automation audit. We'll map your workflows, find the bottlenecks, and show you what's possible.